Quantum Particle Swarm Optimization for Elman Recurrent Network

Particle swarm optimization (PSO) was successfully applied to enhance the classification accuracy in Elman recurrent neural network but the search ability on PSO is still in random. In this paper, a Quantum based approach is implemented to improve the searching ability of the individual particle of PSO. From the experiments, we found the results are promising with quantum techniques and the output is promising.